The system stores a first template which comprises an image of a first validated printed circuit board assembly (PCBA) and stores a first image of an unvalidated PCBA captured prior to shipping of the unvalidated PCBA from a first site to a second site. The system generates a second template which comprises an image of a second validated PCBA corresponding to the first validated PCBA. The system captures a second image of the unvalidated PCBA subsequent to arrival at the second location and a third image prior to installation into a computer system. The system detects an anomaly associated with the unvalidated PCBA based on comparisons between one or more of: the first template and the second template; the first image and the first template; the second image and the second template; the third image and the second template; the first image, the second image, and the third image.

A secondary detection system for integrating automated optical inspection and neural network and a method thereof are disclosed. In the secondary detection system, an automated optical inspection apparatus performs automated optical inspection for pin solder joints on circuit board, and when a detection result indicates abnormal condition, the secondary detection device calculates a detection image probability value based on the component image feature and the template image feature, and calculate pin solder joint image probability values based on the component pin solder joint image feature and the template pin solder joint image feature through siamese neural network, to obtain a minimum probability value among the detection image probability value and pin solder joint image probability values. The minimum probability value is used to determine whether to change the detection result, thereby providing accurate detection result of automated optical inspection and increasing a first pass yield.

An aspect of the disclosed embodiments is a system for printed circuit board (PCB) outline generation including at least one processor configured to receive one or more electronic PCB design files defining a PCB design. The at least one processor is also configured to process the one or more electronic PCB design files to distinguish physical features of the PCB design from non-physical (auxiliary) features of the PCB design. The at least one processor is also configured to generate an estimated physical outline for the PCB design that encompasses the physical features and excludes the auxiliary features. The at least one processor is also configured to electronically store the estimated physical outline in association with the PCB design. Other aspects are included.

An information processing system includes a terminal device and an information processing device. The terminal device captures an image of at least a portion of a printed board and transmits the captured image of the at least a portion of the printed board to the information processing device. Based on the captured image and design information items about a plurality of elements included in the printed board, the information processing device extracts design information items about one or more elements constituting the at least a portion of the printed board, and generates an image in which images based on the design information items about the one or more elements are superimposed on the captured image. The information processing device transmits the generated image to the terminal device. The terminal device displays the generated image, received from the information processing device, on a display of the terminal device.

A defect detection method applied to an electronic device includes determining, pixel difference values based a test sample image and positive sample images. A color difference threshold is determined according to positive sample images. Feature connected regions of the test sample image are generated according to the color difference threshold and pixel difference values. A first threshold is generated according to image noises of positive sample images. A target region is determined from the feature connected regions according to a number of pixel points in each feature connected region and the first threshold. Once a second threshold is determined according to defective pixel points of negative sample images, a detection result of a test sample is determined according to an area of the target region and the second threshold.

This disclosure provides systems, methods, and apparatus detecting defects in a substrate. An image of the substrate is compared with a reference image to identify potential defects. Images corresponding to the potential defects are processed sequentially by a set of classifiers to generate a set of images that include a defect. The set of classifiers can be arranged to have increasing accuracy. A subset of the images corresponding to the potential defects is processed by a type classifier that can determine the type, size, and location of the defect in the images. The defects can be further processed to determine the severity of the defects based on the location of the defects on the substrate.

A registration means for storing an image of a product as a registration image in association with information representing the passing sequence that the product passed through an upstream side process; a management means for managing the matching sequence in a downstream side process; and a matching means for performing matching between an image of a product carried into the downstream side process and the registration image according to the matching sequence, are included. Each time the matching means succeeds in matching, the management means updates the matching sequence to sequence in which registration images not having succeeded in matching with any matching image are put in order on the basis of the passing sequence that the products passed through the upstream side process.

A method for determining wire regions of a circuit includes steps of: obtaining an original image containing multiple stick regions; processing the original image to obtain a first processed image containing multiple line segments; grouping the line segments into multiple groups corresponding respectively to the stick regions; generating a second processed image including multiple complete lines corresponding respectively to the groups; and generating a third processed image including multiple extended lines by extending the complete lines; and determining, for each of the extended lines in the third processed image, a rectangular region based on a stick region in the original image corresponding thereto.

An image processing device that processes a color image in which each pixel has gradation values of three primary colors of RGB includes an image acquiring section to acquire an image; a difference image generating section to use a first primary color image extracted from the color image and a second primary color image in which a gradation value of a second primary color except the first primary color is extracted from the color image, to generate a difference image; a recognition image generating section configured to generate a recognition image having a gradation value obtained by subtracting a gradation value of the difference image from a gradation value of an image in which any one of the three primary colors of RGB is extracted from the color image; and a recognition processing section configured to perform recognition processing of the recognition target using the recognition image.

A board damage classification system includes a Convolutional Neural Network (CNN) sub-engine and a Graph Convolutional Network (GCN) sub-engine that were trained based on digital images of structures that have experienced natural disasters. The CNN sub-engine receives a board digital image of a board, analyzes the board digital image to identify board features, and determines a board feature damage classification for the board features. The CGN sub-engine receives a board feature graph that was generated using the board digital image and that includes nodes that correspond to the board features in the board digital image, and defines relationships between the nodes included in the board feature graph. The board feature damage classification determined by the CNN sub-engine and the relationships defined by the GCN sub-engine are then used to generate a board damage classification that includes a damage probability for board features in the board digital image.